Visible to the public Location Determination by Processing Signal Strength of Wi-Fi Routers in the Indoor Environment with Linear Discriminant Classifier

TitleLocation Determination by Processing Signal Strength of Wi-Fi Routers in the Indoor Environment with Linear Discriminant Classifier
Publication TypeConference Paper
Year of Publication2018
AuthorsAltay, Osman, Ulas, Mustafa
Conference Name2018 6th International Symposium on Digital Forensic and Security (ISDFS)
ISBN Number978-1-5386-3449-3
Keywordsclassification, Classification algorithms, Fingerprint recognition, Global Positioning System, Global Positioning System signals, indoor location determination, indoor radio, Linear discriminant analysis, linear discriminant analysis classification, location determination, machine learning algorithms, Mathematical model, Metrics, pubcrawl, resilience, Resiliency, Router Systems Security, signal classification, statistical analysis, Topology, Wi-Fi routers, Wi-Fi signal strength, Wi-Fi signal values, Wireless fidelity, wireless LAN
Abstract

Location determination in the indoor areas as well as in open areas is important for many applications. But location determination in the indoor areas is a very difficult process compared to open areas. The Global Positioning System (GPS) signals used for position detection is not effective in the indoor areas. Wi-Fi signals are a widely used method for localization detection in the indoor area. In the indoor areas, localization can be used for many different purposes, such as intelligent home systems, locations of people, locations of products in the depot. In this study, it was tried to determine localization for with the classification method for 4 different areas by using Wi-Fi signal values obtained from different routers for indoor location determination. Linear discriminant analysis (LDA) classification was used for classification. In the test using 10k fold cross-validation, 97.2% accuracy value was calculated.

URLhttps://ieeexplore.ieee.org/document/8355353
DOI10.1109/ISDFS.2018.8355353
Citation Keyaltay_location_2018